Scientists at the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information have developed a new search algorithm called FLSHclust that allows for more efficient searching of microbial sequence databases. The algorithm identified 188 new rare CRISPR systems in bacterial genomes, revealing a…
MIT Ignite: Generative AI Entrepreneurship Competition held its first-ever event, where over 100 teams submitted proposals for startups utilizing generative artificial intelligence technologies. Twelve finalists pitched their ideas, covering areas such as health, climate change, education, and workforce dynamics. The competition, co-organized by the MIT-IBM Watson AI Lab and the Martin Trust Center, aimed to…
MIT researchers have developed StableRep, a system that uses synthetic images to train machine learning models, surpassing the results obtained from traditional “real-image” training methods. By using a strategy called “multi-positive contrastive learning,” StableRep considers multiple images generated from the same text prompt as positive pairs, enhancing the model’s understanding of high-level concepts. The approach…
Researchers from MIT, the MIT-IBM Watson AI Lab, and elsewhere have developed PockEngine, an on-device training method that enables deep-learning models to efficiently adapt to new sensor data. The technique significantly speeds up on-device training, performing up to 15 times faster, without sacrificing accuracy. PockEngine also reduces the amount of memory required for fine-tuning. The…
Researchers from MIT, the MIT spinout Inkbit, and ETH Zurich have developed a new 3D inkjet printing system that uses computer vision to adjust the amount of resin each nozzle deposits in real-time. This contactless system allows for the use of materials that cure more slowly than traditional acrylates, enabling the fabrication of complex devices…
Generative AI refers to a machine-learning model that is trained to create new data, instead of making predictions based on existing data. It is different from traditional AI models that focus on prediction tasks. Generative AI has become more powerful with advancements in deep-learning architectures and larger datasets. It is used in various applications, such…
Connectomics, the study of mapping animal brains, is experiencing significant growth. Researchers from MIT and Harvard have developed SmartEM, an electron microscopy technique that utilizes machine learning to analyze brain synapses and neurons at nanometer precision. This integration of hardware and software allows for rapid understanding of complex brain images. SmartEM has the potential to…
CQuotient, a software startup founded by Rama Ramakrishnan, offers personalized recommendations for retailers by diligently noting down customer interactions. The software has been adopted by Salesforce. Ramakrishnan, now a professor at MIT Sloan, teaches students how to apply AI technologies pragmatically. He also guides senior executives in using pre-trained AI models and understanding different categories…
Researchers from MIT and NVIDIA have developed two techniques that can accelerate the processing of sparse tensors, a type of data structure used for high-performance computing. The techniques, called HighLight and Tailors/Swiftiles, can improve the performance and energy-efficiency of hardware accelerators designed for processing sparse tensors. HighLight can efficiently handle various sparsity patterns, while Tailors/Swiftiles…
MIT researchers have developed a search engine, called SecureLoop, that can identify optimal designs for deep neural network accelerators while maintaining data security. The tool considers the impact of adding encryption and authentication measures on performance and energy usage. It improves accelerator designs by boosting performance and keeping data protected, enabling the improvement of AI…
MIT researchers have found evidence suggesting that the brain may develop an intuitive understanding of the physical world through a process similar to self-supervised learning. Using models known as neural networks, they trained them using self-supervised learning techniques and found that the resulting models generated activity patterns similar to those seen in the brains of…
The Kendall Square Association’s 15th annual meeting, titled “Looking Back, Looking Ahead,” allowed members of the community to reflect on the region’s progress and discuss future plans. The event featured talks on recent funding achievements, a panel discussion on artificial intelligence, and a historical tour. The attendees expressed excitement for the future and highlighted Kendall…
MIT engineers have found that deep generative models (DGMs) used in AI can mimic existing designs but struggle to generate innovative solutions to engineering problems. The study showed that when DGMs were designed with engineering objectives in mind, they produced more innovative and higher-performing designs. The researchers concluded that AI models need to go beyond…
Daron Acemoglu, an economist at MIT, has been awarded the prestigious A.SK Social Science Award from the WZB Berlin Social Science Center. The award recognizes his influential work on the role of institutions in capitalist economies, the balance between states and societies, and the risks of automation. Acemoglu, who has made significant contributions to labor…
MIT researchers have developed a machine-learning technique called Diffusion-CCSP that enables robots to efficiently solve complex packing problems. The technique uses a collection of machine-learning models, each representing a specific type of constraint, which are combined to generate global solutions. The method outperformed other techniques, generating a greater number of effective solutions. The researchers aim…
Formal specifications, which use mathematical formulas to describe AI behavior, are not easily interpretable by humans, according to researchers at MIT Lincoln Laboratory. In an experiment, participants were asked to validate an AI agent’s plan for a virtual game based on formal specifications, and they were correct less than half of the time. The researchers…
A team at the MIT Lincoln Laboratory Supercomputing Center (LLSC) is developing techniques to reduce energy consumption in data centers, specifically in relation to artificial intelligence (AI) models. Their methods include power capping hardware and stopping AI training early, with minimal impact on model performance. The team hopes their work will inspire other data centers…
MIT researchers have developed Air-Guardian, an AI system designed to act as a proactive copilot for pilots. The system uses eye-tracking and saliency maps to determine attention and identifies potential risks. It can be adjusted based on the situation’s demands and aims to enhance safety and collaboration in aviation. The system has been tested successfully…
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed Air-Guardian, a system that serves as a proactive copilot for pilots. It uses eye-tracking and saliency maps to determine attention and identifies potential risks. The system can be adjusted based on the situation’s demands and offers a balanced partnership between humans and machines.…
Exciting news! 📣 “Re-imagining the opera of the future” takes center stage once again. 🎭✨ Composer Tod Machover’s groundbreaking opera, “VALIS,” inspired by Philip K. Dick’s science fiction novel, returns after 30 years, re-staged at MIT for a new generation. 🎶🤖 In the mid-1980s, Machover, then in his 20s and the director of musical research…